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Abstract: . Bagging and boosting are methods that generate a diverse ensemble of classifiers by manipulating the training data given to a "base" learning algorithm. Breiman has pointed out that they rely for their effectiveness on the instability of the base learning algorithm. An alternative approach to generating an ensemble is to randomize the internal decisions made by the base algorithm. This general approach has been studied previously by Ali and Pazzani and by Dietterich and Kong. This paper... (Update)
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BibTeX entry: (Update)
Thomas G. Dietterich. An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Unpublished manuscript, 1998. http://citeseer.ist.psu.edu/dietterich98experimental.html More
@article{ dietterich00experimental,
author = "Thomas G. Dietterich",
title = "An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization",
journal = "Machine Learning",
volume = "40",
number = "2",
pages = "139-157",
year = "2000",
url = "citeseer.ist.psu.edu/dietterich98experimental.html" }
Citations (may not include all citations):500 Experiments with a new boosting algorithm - Freund, Schapire - 1996
155 An empirical comparison of voting classification algorithms:.. - Bauer, Kohavi - 1999
89 and arcing classifiers (context) - Breiman
79 Error reduction through learning multiple descriptions - Ali, Pazzani - 1996
62 Pruning adaptive boosting - Margineantu, Dietterich - 1997
51 An empirical evaluation of bagging and boosting - Maclin, Opitz - 1997
41 Heuristics of instability and stabilization in model selecti.. (context) - Breiman - 1994
14 Programs for Empirical Learning (context) - Quinlan - 1993
11 Machine learning bias (context) - Dietterich, Kong - 1995
8 Data mining using MLC - Kohavi, Sommerfield et al. - 1997
5 A comparison of methods for learning and combining evidence .. - Ali - 1995
2 University of California (context) - rep, Statistics
1 UCI repository of machine learning databases (context) - DIETTERICH, Murphy - 1996
The graph only includes citing articles where the year of publication is known.
Documents on the same site (http://www.cs.orst.edu/~tgd/cv/pubs.html): More
An Experimental Comparison of the Nearest-Neighbor and.. - Wettschereck, Dietterich (1995) (Correct)
EDITORIAL Exploratory Research in Machine Learning - Exploratory Research (Correct)
Knowledge Compilation: Bridging the Gap between Specification.. - Dietterich (1991) (Correct)
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